Explore Deep Learning Trends By Decoding Activated Networks
K Gowri, M Nalini Sunil, Rakesh Kumar Yadav · 2023
The dynamic field of deep learning (DL) topologies has enabled deep neural networks to become powerful instruments in various fields, successfully handling complex real-world problems. Activated functions (AFs) are essential elements in these systems that facilitate calculations between both visible and layers that are concealed and are vital to attaining cutting-edge performance. This paper performs a thorough examination of existing AFs used in different machine learning software, highlighting common usage trends. Setting itself apart by covering a broad range of AFs used in DL, this paper highlights their practical uses and common trends and compares them with state-of-the-art results from research. This thorough collection makes it easier to make well-informed decisions and choose the best AF for specific needs. This study is very important because a lot of previous research on AFs tends to repeat previous findings and studies. By contrast, this paper is the first to combine current developments in AF use in real-world applications, which distinguishes it from the large corpus of deep learning research. This research offers a thorough synthesis that deviates from traditional techniques and repetitious findings, contributing to deeper comprehension and use of AFs amid the evolving ecosystem of artificial intelligence. It does this by providing a condensed insight on AF utilization and trends.